Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.Comment: The 30th AAAI Conference on Artificial Intelligence (AAAI-16

A case of complication after a degloving operation of melanoma of the penis—repairing urethrocutaneous fistula with a pedicled gracilis flap

published_or_final_versionSpringer Open Choice, 21 Feb 201

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.Comment: CVPR 201

Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose {\sf Dipole}, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation

Porting or Not Porting? Availability of Exclusive Games in the Mobile App Market

Mobile games dominate the mobile app markets and contribute over half of the mobile app revenues. In order to attract more users and generate higher revenues, the platforms such as Apple iOS and Google Android, would like to partner with the game developers and have the developers exclusively stay at their own platforms to entice more consumer demands. For example, the game developer “Electronic Arts” agrees to offer Apple iOS a two-month exclusive window for the well-known mobile game “Plants vs. Zombies 2”. The benefits of the exclusivity to the platforms and app developers are unclear and not studies in the literature. This study aims (1) to provide managerial insights for the platforms and app developers, and (2) to analyze the pros and cons of the partnership strategy, e.g. when offering an exclusive deal, how do the platforms and developers maximize the corresponding profits by the exclusive deal, and what is the optimal exclusive duration

Topological analysis of a haloacid permease of a Burkholderia sp. bacterium with a PhoA-LacZ reporter

<p>Abstract</p> <p>Background</p> <p>2-Haloacids can be found in the natural environment as degradative products of natural and synthetic halogenated compounds. They can also be generated by disinfection of water and have been shown to be mutagenic and to inhibit glyceraldehyde-3-phosphate dehydrogenase activity. We have recently identified a novel haloacid permease Deh4p from a bromoacetate-degrading bacterium <it>Burkholderia </it>sp. MBA4. Comparative analyses suggested that Deh4p is a member of the Major Facilitator Superfamily (MFS), which includes thousands of membrane transporter proteins. Members of the MFS usually possess twelve putative transmembrane segments (TMS). Deh4p was predicted to have twelve TMS. In this study we characterized the topology of Deh4p with a PhoA-LacZ dual reporters system.</p> <p>Results</p> <p>Thirty-six Deh4p-reporter recombinants were constructed and expressed in <it>E. coli</it>. Both PhoA and LacZ activities were determined in these cells. Strength indices were calculated to determine the locations of the reporters. The results mainly agree with the predicted model. However, two of the TMS were not verified. This lack of confirmation of the TMS, using a reporter, has been reported previously. Further comparative analysis of Deh4p has assigned it to the Metabolite:H⁺Symporter (MHS) 2.A.1.6 family with twelve TMS. Deh4p exhibits many common features of the MHS family proteins. Deh4p is apparently a member of the MFS but with some atypical features.</p> <p>Conclusion</p> <p>The PhoA-LacZ reporter system is convenient for analysis of the topology of membrane proteins. However, due to the limitation of the biological system, verification of some of the TMS of the protein was not successful. The present study also makes use of bioinformatic analysis to verify that the haloacid permease Deh4p of <it>Burkholderia </it>sp. MBA4 is a MFS protein but with atypical features.</p

CEO Compensation, Compensation Risk, and Corporate Governance: Evidence from Technology Firms

Literature suggests that CEOs of technology firms earn higher pay than CEOs of non-technology firms. I investigate whether compensation risk explains the difference in compensation between technology firms and non-technology firms. Controlling for firm size and performance, I find that CEOs in technology firms have higher pay, but also have much higher compensation risk compared to non-technology firms. Compensation risk explains the major part of the difference in CEO pay. My study is consistent with the labor market economics view that CEOs earn competitive risk-adjusted total compensation